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64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
from __future__ import unicode_literals, print_function
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import json
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import pathlib
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import random
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import spacy
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from spacy.pipeline import EntityRecognizer
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from spacy.gold import GoldParse
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def train_ner(nlp, train_data, entity_types):
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ner = EntityRecognizer(nlp.vocab, entity_types=entity_types)
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for itn in range(5):
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random.shuffle(train_data)
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for raw_text, entity_offsets in train_data:
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doc = nlp.make_doc(raw_text)
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gold = GoldParse(doc, entities=entity_offsets)
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ner.update(doc, gold)
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ner.model.end_training()
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return ner
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def main(model_dir=None):
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if model_dir is not None:
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model_dir = pathlib.Path(model_dir)
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if not model_dir.exists():
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model_dir.mkdir()
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assert model_dir.is_dir()
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nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
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train_data = [
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(
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'Who is Shaka Khan?',
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[(len('Who is '), len('Who is Shaka Khan'), 'PERSON')]
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),
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(
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'I like London and Berlin.',
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[(len('I like '), len('I like London'), 'LOC'),
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(len('I like London and '), len('I like London and Berlin'), 'LOC')]
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)
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]
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ner = train_ner(nlp, train_data, ['PERSON', 'LOC'])
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doc = nlp.make_doc('Who is Shaka Khan?')
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nlp.tagger(doc)
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ner(doc)
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for word in doc:
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print(word.text, word.tag_, word.ent_type_, word.ent_iob)
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if model_dir is not None:
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with (model_dir / 'config.json').open('w') as file_:
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json.dump(ner.cfg, file_)
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ner.model.dump(str(model_dir / 'model'))
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if __name__ == '__main__':
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main()
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# Who "" 2
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# is "" 2
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# Shaka "" PERSON 3
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# Khan "" PERSON 1
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# ? "" 2
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